OpenRaiser/Intern-Atlas huggingface dataset:
Uncertainty-Aware Collaborative System of Large and Small Models for Multimodal Sentiment Analysis
Venue: arXiv cs.CL 2026
Year: 2026

Multimodal Large Language Models (MLLMs) have notably enhanced the performance of Multimodal Sentiment Analysis (MSA), yet their massive parameter scale leads to excessive resource consumption in training and inference, severely limiting model efficiency. To balance performance and efficiency for MSA, this paper innovatively proposes a novel Uncertainty-Aware Collaborative System (U-ACS) that integrates Uncertainty-aware Baseline Model (UBM) with MLLMs. U-ACS operates in three stages: First, all samples are processed by the UBM, retain high-confidence samples and forward low-confidence samples to the MLLM. Notably, to address the challenge that continuous outputs of regression tasks hinder uncertainty calculation, we innovatively convert the continuous sentiment label prediction task to a classification task, enabling a more accurate calculation of entropy and uncertainty. Second, the MLLM performs initial process. In this stage, high-confidence samples or low-confidence samples whose predictive sentiment polarity matches that of the UBM are deemed acceptable, while unqualified samples are forwarded for further processing. Finally, the MLLM performs secondary inference on remaining low-confidence samples using prompts augmented with prior rounds predictions as references. By aggregating results from the three stages, U-ACS preserves high MSA prediction accuracy while drastically boosting efficiency via offloading most simple samples to the UBM and minimizing MLLM processing volume. Extensive experiments verify that U-ACS maintains superior performance while significantly reducing computational overhead and resource consumption.
Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model
Venue: arXiv cs.CV 2026
Year: 2026

Recent advances in multimodal models have demonstrated impressive capabilities in unified image generation and editing. However, many prominent open-source models prioritize scaling model parameters over optimizing training strategies, limiting their efficiency and performance. In this work, we present UniPic2-SD3.5M-Kontext, a 2B-parameter DiT model based on SD3.5-Medium, which achieves state-of-the-art image generation and editing while extending seamlessly into a unified multimodal framework. Our approach begins with architectural modifications to SD3.5-Medium and large-scale pre-training on high-quality data, enabling joint text-to-image generation and editing capabilities. To enhance instruction following and editing consistency, we propose a novel Progressive Dual-Task Reinforcement strategy (PDTR), which effectively strengthens both tasks in a staged manner. We empirically validate that the reinforcement phases for different tasks are mutually beneficial and do not induce negative interference. After pre-training and reinforcement strategies, UniPic2-SD3.5M-Kontext demonstrates stronger image generation and editing capabilities than models with significantly larger generation parameters-including BAGEL (7B) and Flux-Kontext (12B). Furthermore, following the MetaQuery, we connect the UniPic2-SD3.5M-Kontext and Qwen2.5-VL-7B via a connector and perform joint training to launch a unified multimodal model UniPic2-Metaquery. UniPic2-Metaquery integrates understanding, generation, and editing, achieving top-tier performance across diverse tasks with a simple and scalable training paradigm. This consistently validates the effectiveness and generalizability of our proposed training paradigm, which we formalize as Skywork UniPic 2.0.
Nexus: Efficient and Scalable Multi-Cell mmWave Baseband Processing with Heterogeneous Compute
Venue: arXiv cs.NI 2026
Year: 2026

The rapid adoption of 5G New Radio (NR), particularly in the millimeter-wave (mmWave) spectrum, imposes stringent demands on the flexibility, scalability, and efficiency of baseband processing. While virtualized Radio Access Networks (vRANs) enable dynamic spectrum sharing across cells, compute resource allocation for baseband processing, especially in multi-cell deployments with heterogeneous workloads, remains underexplored. In this paper, we present NEXUS, the first system to realize real-time, virtualized multi-cell mmWave baseband processing on a single server with heterogeneous compute resources. NEXUS integrates software-based digital signal processing pipelines with hardware-accelerated LDPC decoding, and introduces a novel framework for sharing Intel's ACC100 eASIC across multiple CPU cores via virtual functions (VFs). For single-cell operation, NEXUS employs a random forest (RAF)-based model that predicts the most energy-efficient resource allocation for the given cell configuration with microsecond-level inference latency and high accuracy. For multi-cell scenarios, NEXUS introduces a power-aware scheduler that incorporates a lightweight contention model to adjust resource allocation strategies under concurrent execution. Through extensive evaluation across various Frequency Range 2 (FR2) cell configurations, we show that NEXUS supports up to 16 concurrent cells under full load, achieving 5.37Gbps aggregate throughput, while reducing the multi-cell scheduling search space by orders of magnitude. These results demonstrate that virtualized, resource-aware baseband processing is both practical and efficient for next-generation vRAN systems.
Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair
Venue: arXiv cs.SE 2026
Year: 2026

Large Language Model (LLM) - based Automated Program Repair (APR) systems are increasingly integrated into modern software development workflows, offering automated patches in response to natural language bug reports. However, this reliance on untrusted user input introduces a novel and underexplored attack surface. In this paper, we investigate the security risks posed by adversarial bug reports -- realistic-looking issue submissions crafted to mislead APR systems into producing insecure or harmful code changes. We develop a comprehensive threat model and conduct an empirical study to evaluate the vulnerability of APR systems to such attacks. Our demonstration comprises 51 adversarial bug reports generated across a spectrum of strategies, ranging from manual curation to fully automated pipelines. We test these against a leading LLM-based APR system and assess both pre-repair defenses (e.g., LlamaGuard variants, PromptGuard variants, Granite-Guardian, and custom LLM filters) and post-repair detectors (GitHub Copilot, CodeQL). Our findings show that current defenses are insufficient: 90% of crafted bug reports triggered attacker-aligned patches. The best pre-repair filter blocked only 47%, while post-repair analysis -- often requiring human oversight -- was effective in just 58% of cases. To support scalable security testing, we introduce a prototype framework for automating the generation of adversarial bug reports. Our analysis exposes a structural asymmetry: generating adversarial inputs is inexpensive, while detecting or mitigating them remains costly and error-prone. We conclude with recommendations for improving the robustness of APR systems against adversarial misuse and highlight directions for future work on secure APR.
Collaborate, Deliberate, Evaluate: How LLM Alignment Affects Coordinated Multi-Agent Outcomes
Venue: arXiv cs.CL 2026
Year: 2026

As Large Language Models (LLMs) get integrated into diverse workflows, they are increasingly being regarded as "collaborators" with humans, and required to work in coordination with other AI systems. If such AI collaborators are to reliably coordinate their actions and behaviors with humans or other AIs, their properties and behaviors over multi-turn interactions must be known and predictable. This paper examines how different alignment methods affect LLM agents' effectiveness as partners in multi-turn, multi-party collaborations. We study this question through the lens of intervention agents that insert themselves into group dialogues not to provide answers, but to encourage the collaborative group to slow down and reflect upon their reasoning for deliberative decision-making. Common alignment techniques are typically developed under simplified single-user settings and assume the optimality of the underlying token MDP. Using the theoretical lens of the modified-action MDP, we show how they do not account for the dynamics of long-horizon multi-party interactions. We present a novel roleplay simulation methodology, where we align LLMs according to different methods and then deploy them in collaborative task dialogues to quantify how interventions affect the trajectory of group collaboration, belief alignment, and coordination. Our results show that an intervention agent that is robust to action modification significantly outperforms common alignment baselines in supporting correct task outcomes.
The Keychain Problem: On Minimizing the Opportunity Cost of Uncertainty
Venue: arXiv cs.GT 2026
Year: 2026

In this paper, we introduce a family of sequential decision-making problems, collectively termed the Keychain Problem, that involve exploring a set of actions to maximize expected payoff when only a subset of actions are available in each stage. In an instance of the Keychain Problem, a locksmith faces a sequence of decisions, each of which involves selecting one key from a keychain (a subset of keys) to attempt to open a lock. Given a Bayesian prior on the effectiveness of keys, the locksmith's goal is to minimize the opportunity cost, which is the expected number of rounds in which the chain has a correct key but our selected key is incorrect. We study the computation of the Bayes optimal solution for Keychain Problems. Employing polynomial-time reductions, we establish formal connections between natural variants of the Keychain Problem and well-studied algorithmic economics problems on bipartite graphs. When the keychain order is known to the locksmith, we show that it reduces to Maximum Weight Bipartite Matching (MWBM). More general is the situation when the keychain order is sampled from a prior distribution (possibly correlated with the correct key). Here the Keychain Problem reduces to a novel generalization of MWBM which we coin the Maximum Weight Laminar Matching, which then further reduces to combinatorial auctions under XOS valuation functions. Finally, we show that when the locksmith can choose the keychain order, the Keychain problem reduces from a classic NP-hard combinatorial problem, again, on bipartite graphs. Besides implying algorithmic results and deepening our structural understanding about the Keychain Problem, our established reductions also find applications beyond -- for example, to the Philosopher Inequality for online bipartite matching.
Optimal Average Disk-Inspection via Fermat's Principle
Venue: arXiv cs.DM 2026
Year: 2026

This work resolves the optimal average-case cost of the Disk-Inspection problem, a variant of Bellman's 1955 lost-in-a-forest problem. In Disk-Inspection, a mobile agent starts at the center of a unit disk and follows a trajectory that inspects perimeter points whenever the disk does not obstruct visibility. The worst-case cost was solved optimally in 1957 by Isbell, but the average-case version remained open, with heuristic upper bounds proposed by Gluss in 1961 and improved only recently. Our approach applies Fermat's Principle of Least Time to a recently proposed discretization framework, showing that optimal solutions are captured by a one-parameter family of recurrences independent of the discretization size. In the continuum limit these recurrences give rise to a single-parameter optimal control problem, whose trajectories coincide with limiting solutions of the original Disk-Inspection problem. A crucial step is proving that the optimal initial condition generates a trajectory that avoids the unit disk, thereby validating the optics formulation and reducing the many-variable optimization to a rigorous one-parameter problem. In particular, this disproves Gluss's conjecture that optimal trajectories must touch the disk. Our analysis determines the exact optimal average-case inspection cost, equal to $3.549259\ldots$ and certified to at least six digits of accuracy.
Reason to Retrieve: Enhancing Query Understanding through Decomposition and Interpretation
Venue: arXiv cs.IR 2026
Year: 2026

Query understanding (QU) aims to accurately infer user intent to improve document retrieval. It plays a vital role in modern search engines. While large language models (LLMs) have made notable progress in this area, their effectiveness has primarily been studied on short, keyword-based queries. With the rise of AI-driven search, long-form queries with complex intent become increasingly common, but they are underexplored in the context of LLM-based QU. To address this gap, we introduce ReDI, a reasoning-enhanced query understanding method through decomposition and interpretation. ReDI uses the reasoning and understanding capabilities of LLMs within a three-stage pipeline. (i) It decomposes a complex query into a set of targeted sub-queries to capture the user intent. (ii) It enriches each sub-query with detailed semantic interpretations to enhance the retrieval of intent-document matching. And (iii), after independently retrieving documents for each sub-query, ReDI uses a fusion strategy to aggregate the results and obtain the final ranking. We collect a large-scale dataset of real-world complex queries from a commercial search engine and distill the query understanding capabilities of DeepSeek-R1 into small models for practical application. Experiments on public benchmarks, including BRIGHT and BEIR, show that ReDI consistently outperforms strong baselines in both sparse and dense retrieval paradigms, demonstrating its effectiveness. We release our code, generated sub-queries, and interpretations at https://github.com/youngbeauty250/ReDI.
On the (In)Security of Loading Machine Learning Models
Venue: arXiv cs.CR 2026
Year: 2026

The rise of model sharing through frameworks and dedicated hubs makes Machine Learning significantly more accessible. Despite its benefits, loading shared models exposes users to underexplored security risks, while security awareness remains limited among both practitioners and developers. To enable a more security-conscious approach in Machine Learning model sharing, in this paper, we evaluate the security posture of frameworks and hubs, assess whether security-oriented mechanisms offer real protection, and survey how users perceive the security narratives surrounding model sharing. Our evaluation shows that most frameworks and hubs address security risks partially at best, often by shifting responsibility to the user. More concerningly, our analysis of frameworks advertising security-oriented settings and complete model sharing uncovered multiple 0-day vulnerabilities enabling arbitrary code execution. Through this analysis, we show that, despite the recent narrative, securely loading Machine Learning models is far from being a solved problem and cannot be guaranteed by the file format used for sharing. Our survey shows that the security narrative leads users to consider security-oriented settings as trustworthy, despite the weaknesses shown in this work. From this, we derive suggestions to strengthen the security of model-sharing ecosystems.
RAFFLES: Reasoning-based Attribution of Faults for LLM Systems
Venue: arXiv cs.AI 2026
Year: 2026

The advent of complex, interconnected long-horizon LLM systems has made it incredibly tricky to identify where and when these systems break down. Evaluation capabilities that currently exist today are limited in that they often focus on simple metrics, end-to-end outcomes, and are dependent on the perspectives of humans. In order to match the increasing complexity of these many component systems, evaluation frameworks must also be able to reason, probe, iterate, and understand the nuanced logic passing through these systems. In this paper, we present RAFFLES, an offline evaluation architecture that incorporates iterative reasoning. Specifically, RAFFLES operates as an iterative, multi-component pipeline, using a central Judge to systematically identify faults and a set of specialized Evaluators to assess the quality of the candidate faults as well as rationales of the Judge. We evaluated RAFFLES with several benchmarks - the Who&When dataset to identify step-level faults in multi-agent systems and the ReasonEval datasets to diagnose step-level mathematical reasoning errors. RAFFLES outperforms strong baselines, achieving an accuracy of over 20% and 50% on the Who&When Hand-Crafted and Algorithmically-Generated datasets, and over 80% on the ReasonEval datasets. These results demonstrate a key step towards introducing automated fault detection for autonomous systems over labor-intensive manual review.
Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data
Venue: arXiv cs.SD 2026
Year: 2026

Large language models (LLMs) have transformed NLP, yet their integration with audio remains underexplored despite audio's centrality to human communication. We introduce Falcon3-Audio, a family of Audio-Language Models (ALMs) built on instruction-tuned LLMs and Whisper encoders. Using a remarkably small amount of public audio data, less than 30K hours (5K unique), Falcon3-Audio-7B matches the best reported performance among open-weight models on the MMAU benchmark, with a score of 64.14, matching R1-AQA, while distinguishing itself through superior data and parameter efficiency, single-stage training, and transparency. Notably, our smallest 1B model remains competitive with larger open models ranging from 2B to 13B parameters. Through extensive ablations, we find that common complexities such as curriculum learning, multiple audio encoders, and intricate cross-attention connectors are not required for strong performance, even compared to models trained on over 500K hours of data.
Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
Venue: arXiv cs.LG 2026
Year: 2026

Physics-informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs) relevant to multiscale modeling, but they often fail when applied to materials with discontinuous coefficients, such as media with piecewise constant properties. This paper introduces a dual formulation for the PINN framework to improve the reliability of the homogenization of periodic thermo-conductive composites, for both strong and variational (weak) formulations. The dual approach facilitates the derivation of guaranteed upper and lower error bounds, enabling more robust detection of PINN failure. We compare standard PINNs applied to smoothed material approximations with variational PINNs (VPINNs) using both spectral and neural network-based test functions. Our results indicate that while strong-form PINNs may outperform VPINNs in controlled settings, they are sensitive to material discontinuities and may fail without clear diagnostics. In contrast, VPINNs accommodate piecewise constant material parameters directly but require careful selection of test functions to avoid instability. Dual formulation serves as a reliable indicator of convergence quality, and its integration into PINN frameworks enhances their applicability to homogenization problems in micromechanics.
SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
Venue: arXiv cs.CL 2026
Year: 2026

We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It addresses critical limitations in OpenAI's benchmark, including noisy and incorrect labels, topical biases, and question redundancy. SimpleQA Verified was created through a rigorous multi-stage filtering process involving de-duplication, topic balancing, and source reconciliation to produce a more reliable and challenging evaluation set, alongside improvements in the autorater prompt. On this new benchmark, Gemini 2.5 Pro achieves a state-of-the-art F1-score of 55.6, outperforming other frontier models, including GPT-5. This work provides the research community with a higher-fidelity tool to track genuine progress in parametric model factuality and to mitigate hallucinations. The benchmark dataset, evaluation code, and leaderboard are available at: https://www.kaggle.com/benchmarks/deepmind/simpleqa-verified.
MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

Large language models excel in English but still struggle with complex reasoning in many low-resource languages (LRLs). Existing encoder-plus-decoder methods such as LangBridge and MindMerger raise accuracy on mid and high-resource languages, yet they leave a large gap on LRLs. We present MERLIN, a two-stage model-stacking framework that applies a curriculum learning strategy -- from general bilingual bitext to task-specific data -- and adapts only a small set of DoRA weights. On the AfriMGSM benchmark MERLIN improves exact-match accuracy by +12.9 pp over MindMerger and outperforms GPT-4o-mini. It also yields consistent gains on MGSM and MSVAMP (+0.9 and +2.8 pp), demonstrating effectiveness across both low and high-resource settings.
EFPIX: A zero-trust encrypted flood protocol
Venue: arXiv cs.CR 2026
Year: 2026

We propose EFPIX (Encrypted Flood Protocol for Information eXchange), a flood-based relay communication protocol that achieves end-to-end encryption, plausible deniability for users, and untraceable messages while hiding metadata, such as sender and receiver, from those not involved. It also has built-in spam resistance and multiple optional enhancements. It can be used in privacy-critical communication, infrastructure-loss scenarios, space/research/military communication, where central servers are infeasible, or general-purpose messaging.
Efficiently Computing Equilibria in Budget-Aggregation Games
Venue: arXiv cs.GT 2026
Year: 2026

Budget aggregation deals with the social choice problem of distributing an exogenously given budget among a set of public projects, given agents' preferences. Taking a game-theoretic perspective, we study budget-aggregation games where each agent has virtual decision power over some fraction of the budget. We investigate the structure and show efficient computability of Nash equilibria for various common preference models in this setting. In particular, we show that equilibria for Leontief utilities can be found in polynomial time, solving an open problem from Brandt et al. [2023], and give an explicit polynomial-time algorithm for computing equilibria for $\ell_1$ preferences.
Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
Venue: arXiv cs.AI 2026
Year: 2026

Markets increasingly accommodate large language models (LLMs) as autonomous decision-making agents. As this transition occurs, it becomes critical to evaluate how these agents behave relative to their human and task-specific statistical predecessors. In this work, we present results from an empirical study comparing humans (N=216), multiple frontier LLMs, and customized Bayesian agents in dynamic multi-player bargaining games under identical conditions. Bayesian agents extract the highest surplus with aggressive trade proposals that are frequently rejected. Humans and LLMs achieve comparable aggregate surplus within their groups, but exhibit different trading strategies. LLMs favor conservative, concessionary proposals that are usually accepted by other LLMs, while humans propose trades that are consistent with fairness norms but are more likely to be rejected. These findings highlight that performance parity -- a common benchmark in agent evaluation -- can mask substantive procedural differences in how LLMs behave in complex multi-agent interactions.